Data Science Training by Experts

;

Our Training Process

Data Science - Syllabus, Fees & Duration

MODULE 1

  • The Data Science Process
  • Apply the CRISP-DM process to business applications
  • Wrangle, explore, and analyze a dataset
  • Apply machine learning for prediction
  • Apply statistics for descriptive and inferential understanding
  • Draw conclusions that motivate others to act on your results

MODULE 2

  • Communicating with Stakeholders
  • Implement best practices in sharing your code and written summaries
  • Learn what makes a great data science blog
  • Learn how to create your ideas with the data science community

MODULE 3

  • Software Engineering Practices
  • Write clean, modular, and well-documented code
  • Refactor code for efficiency
  • Create unit tests to test programs
  • Write useful programs in multiple scripts
  • Track actions and results of processes with logging
  • Conduct and receive code reviews

MODULE 4

  • Object Oriented Programming
  • Understand when to use object oriented programming
  • Build and use classes
  • Understand magic methods
  • Write programs that include multiple classes, and follow good code structure
  • Learn how large, modular Python packages, such as pandas and scikit-learn, use object oriented programming
  • Portfolio Exercise: Build your own Python package

MODULE 5

  • Web Development
  • Learn about the components of a web app
  • Build a web application that uses Flask, Plotly, and the Bootstrap framework
  • Portfolio Exercise: Build a data dashboard using a dataset of your choice and deploy it to a web application

MODULE 6

  • ETL Pipelines
  • Understand what ETL pipelines are
  • Access and combine data from CSV, JSON, logs, APIs, and databases
  • Standardize encodings and columns
  • Normalize data and create dummy variables
  • Handle outliers, missing values, and duplicated data
  • Engineer new features by running calculations • Build a SQLite database to store cleaned data

MODULE 7

  • Natural Language Processing
  • Prepare text data for analysis with tokenization, lemmatization, and removing stop words
  • Use scikit-learn to transform and vectorize text data
  • Build features with bag of words and tf-idf
  • Extract features with tools such as named entity recognition and part of speech tagging
  • Build an NLP model to perform sentiment analysis

MODULE 8

  • Machine Learning Pipelines
  • Understand the advantages of using machine learning pipelines to streamline the data preparation and modeling process
  • Chain data transformations and an estimator with scikit- learn’s Pipeline
  • Use feature unions to perform steps in parallel and create more complex workflows
  • Grid search over pipeline to optimize parameters for entire workflow
  • Complete a case study to build a full machine learning pipeline that prepares data and creates a model for a dataset

MODULE 9

  • Experiment Design
  • Understand how to set up an experiment, and the ideas associated with experiments vs. observational studies
  • Defining control and test conditions
  • Choosing control and testing groups

MODULE 10

  • Statistical Concerns of Experimentation
  • Applications of statistics in the real world
  • Establishing key metrics
  • SMART experiments: Specific, Measurable, Actionable, Realistic, Timely

MODULE 11

  • A/B Testing
  • How it works and its limitations
  • Sources of Bias: Novelty and Recency Effects
  • Multiple Comparison Techniques (FDR, Bonferroni, Tukey)
  • Portfolio Exercise: Using a technical screener from Starbucks to analyze the results of an experiment and write up your findings

MODULE 12

  • Introduction to Recommendation Engines
  • Distinguish between common techniques for creating recommendation engines including knowledge based, content based, and collaborative filtering based methods.
  • Implement each of these techniques in python.
  • List business goals associated with recommendation engines, and be able to recognize which of these goals are most easily met with existing recommendation techniques.

MODULE 13

  • Matrix Factorization for Recommendations
  • Understand the pitfalls of traditional methods and pitfalls of measuring the influence of recommendation engines under traditional regression and classification techniques.
  • Create recommendation engines using matrix factorization and FunkSVD
  • Interpret the results of matrix factorization to better understand latent features of customer data
  • Determine common pitfalls of recommendation engines like the cold start problem and difficulties associated with usual tactics for assessing the effectiveness of recommendation engines using usual techniques, and potential solutions.

Download Syllabus - Data Science
Course Fees
10000+
20+
50+
25+

Data Science Jobs in Saskatoon

Enjoy the demand

Find jobs related to Data Science in search engines (Google, Bing, Yahoo) and recruitment websites (monsterindia, placementindia, naukri, jobsNEAR.in, indeed.co.in, shine.com etc.) based in Saskatoon, chennai and europe countries. You can find many jobs for freshers related to the job positions in Saskatoon.

  • Data Scientist
  • Data Analyst
  • Data Engineer
  • Data Storyteller
  • Machine Learning Scientist
  • Machine Learning Engineer
  • Business Intelligence Developer
  • Database Administrator
  • ML Engineer
  • Computer Vision Engineer

Data Science Internship/Course Details

Data Science internship jobs in Saskatoon
Data Science To find trends and patterns, use algorithms and modules. Create data strategies with the help of team members and leaders. Creative thinking, problem-solving skills, curiosity, and a drive to learn about and investigate industry trends and development, as well as teamwork, are among the soft skills required by data scientists. Experts provide immersive online instructor-led seminars. A Data Scientist is a highly skilled someone with advanced mathematical, statistical, scientific, analytical, and technical abilities who can prepare, clean, and validate organized and unstructured data for industries to utilize in making better decisions. The top Data Science course online for professionals who wish to expand their knowledge base and start a career in this industry is NESTSOFT in Saskatoon. There are numerous reasons why you should take this course. You may learn all of the skills and talents required to become a data scientist by enrolling in the top data science online courses in Saskatoon. . Data Science provides a diverse set of tools for analyzing data from a range of sources, including financial records, multimedia files, marketing forms, sensors, and text files.

List of All Courses & Internship by TechnoMaster

Success Stories

The enviable salary packages and track record of our previous students are the proof of our excellence. Please go through our students' reviews about our training methods and faculty and compare it to the recorded video classes that most of the other institutes offer. See for yourself how TechnoMaster is truly unique.

List of Training Institutes / Companies in Saskatoon

  • SaskatchewanPolytechnic | Location details: 55 33 St E, Saskatoon, SK S7K 0R8 | Classification: College, College | Visit Online: saskpolytech.ca | Contact Number (Helpline): +1 866-467-4278
  • SaskatchewanPolytechnic | Location details: 55 33 St E, Saskatoon, SK S7K 0R8 | Classification: College, College | Visit Online: saskpolytech.ca | Contact Number (Helpline): +1 866-467-4278
  • SaskatchewanPolytechnic | Location details: 55 33 St E, Saskatoon, SK S7K 0R8, Canada | Classification: College, College | Visit Online: saskpolytech.ca | Contact Number (Helpline): +1 866-467-4278
 courses in Saskatoon
1% had been Aboriginal, and 30. There is likewise room for improvement. Its ratings for bodily fitness and nicely-being, and emotional fitness and maturity, had been near Canadian norms. Results primarily based totally at the Early Development Instrument, a degree derived from reviews with the aid of using kids`s kindergarten teachers, indicated that kids in Saskatoon fare in particular nicely in social competence, and communique competencies and preferred knowledge. It stands in comparison to ``authoritarian`` parenting, characterized with the aid of using dad and mom being fairly controlling and extremely harsh in their method to discipline, and "permissive" parenting, characterized with the aid of using dad and mom being overly-indulgent and putting few limits for behaviour. Studies in one pilot network and 5 have a look at groups had been carried out in 2000-2001. Each assessment comprised numerous measures: X Family heritage consists of records at the dad and mom' earnings, stage of schooling, and occupational popularity. 6% in comparison with 69. X The dad and mom' stage of schooling, whether or not the dad and mom had been operating out of doors the house, social guide, and use of network assets had been the maximum critical variables related to the cognitive area. Parents are engaged with their kids and make use of network assets.

Trained more than 10000+ students who trust Nestsoft TechnoMaster

Get Your Personal Trainer